Collision and injury mitigation systems (C&IMSs) are becoming more widely used. C&IMSs provide a vehicle operator and/or vehicle knowledge and awareness of objects within a close proximity so as to prevent colliding with those objects. C&IMSs are also helpful in mitigation of an injury to a vehicle occupant in the event of an unavoidable collision.
Several types of C&IMSs use millimeter wave radar or laser radar in measuring distance between a host vehicle and an object. Radar based C&IMSs transmit and receive signals from various objects including roadside clutter, within a close proximity, to a host vehicle.
C&IMSs discern, from acquired radar data, and report whether a detected object is a potential unsafe object or a potential safe object. Current C&IMSs are able to discern whether an object is a potential unsafe object or a potential safe object to some extent, but yet there still exists situations when objects are misclassified.
Four situations can arise with object recognition by radar based C&IMSs. The four situations are referred to as: a positive real threat situation, a negative real threat situation, a negative false threat situation, and a positive false threat situation.
A positive real threat situation refers to a situation when an unsafe and potential collision-causing object, such as a stopped vehicle directly in the path of a host vehicle exists and is correctly identified to be a threatening object. This accurate assessment is a highly desirable requirement and is vital to deployment of active safety countermeasures.
A negative real threat situation refers to a situation when an unsafe and potential collision-causing object exists, but is incorrectly identified as a non-threatening object. This erroneous assessment is a highly undesirable requirement as it renders the C&IMS ineffective.
A negative false threat situation refers to a situation when an unsafe object does not exist in actuality, and is correctly identified as a non-threatening object. This accurate assessment is a highly desirable requirement and is vital to non-deployment of active safety countermeasures.
A positive false threat situation refers to a situation when an unsafe object does not exist in actuality, but is incorrectly identified as a threatening object. For example, a stationary roadside object may be identified as a potentially collision causing object when in actuality it is a non-threatening object. Additionally, a small object may be in the path of the host vehicle and, although in actuality it is not a potential threat to the host vehicle, but is misclassified as a potentially unsafe object. This erroneous assessment is a highly undesirable requirement as it will be a nuisance to active safety countermeasures.
Accurate assessment of objects is desirable for deployment of active safety countermeasures. Erroneous assessment of objects may cause active safety countermeasures to perform or activate improperly and therefore render a C&IMS ineffective.
Additionally, C&IMSs may inadvertently generate false objects, which are sometimes referred to in the art as ghost objects. Ghost objects are objects that are detected by a C&IMS, which in actuality do not exist or are incorrectly generated by the C&IMS.
Many C&IMSs use triangulation to detect and classify objects. In using triangulation a C&IMS can potentially, in certain situations, artificially create ghost objects.
During triangulation multiple sensors are used to detect radar echoes returning from an object and determine ranges between the sensors and the object. Circular arcs are then created having centers located at the sensors and radius equal to the respective ranges to the object. Where the arcs from the multiple sensors intersect is where an object is assumed to be located.
Intersections of the arcs that are associated with the same detected object, yield location of real objects. Intersections of arcs associated with different detected objects produce ghost objects.
The number of ghost objects that may potentially be created is related to the amount of real objects detected. The following expression represents the approximate peak amount of ghost objects that may be created from real objects detected by a four sensor system using a triangulation technique:G=6*(R^2−R)  1where R is the number of real objects and G is the number of false objects.
Sensor signals are noisy due to the nature of sensor properties. C&IMS that traditionally use direct sensor data, produce inaccurate triangulation intersections in response to the data. As a result, a suspected object location appears as a “spread-out” and moving conglomeration or cluster of intersections. This gives rise to inaccuracy in pinpointing the object. Accurate estimation and tracking of the cluster movement is vital to successful performance of a C&IMS.
Also, traditional C&IMSs by directly using sensor data from single or multiple sensors, can exhibit false measurements, due to items such as multiple paths, echoing, or misfiring of the sensors. These false measurements produce additional false objects and further increase difficulty in properly classifying objects.
An ongoing concern for safety engineers is to provide a safer automotive vehicle with increased collision and injury mitigation intelligence as to decrease the probability of a collision or an injury. Therefore, it would be desirable to provide an improved C&IMS that is able to better classify detected objects over traditional C&IMSs.